Overview

Dataset statistics

Number of variables18
Number of observations8950
Missing cells314
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory144.0 B

Variable types

Categorical1
Numeric17

Alerts

CUST_ID has a high cardinality: 8950 distinct values High cardinality
BALANCE is highly correlated with BALANCE_FREQUENCY and 4 other fieldsHigh correlation
BALANCE_FREQUENCY is highly correlated with BALANCE and 1 other fieldsHigh correlation
PURCHASES is highly correlated with ONEOFF_PURCHASES and 5 other fieldsHigh correlation
ONEOFF_PURCHASES is highly correlated with PURCHASES and 2 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly correlated with PURCHASES and 3 other fieldsHigh correlation
CASH_ADVANCE is highly correlated with BALANCE and 2 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly correlated with PURCHASES and 3 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly correlated with PURCHASES and 2 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly correlated with PURCHASES and 3 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly correlated with BALANCE and 2 other fieldsHigh correlation
PURCHASES_TRX is highly correlated with PURCHASES and 5 other fieldsHigh correlation
MINIMUM_PAYMENTS is highly correlated with BALANCE and 1 other fieldsHigh correlation
BALANCE is highly correlated with CREDIT_LIMITHigh correlation
PURCHASES is highly correlated with ONEOFF_PURCHASES and 3 other fieldsHigh correlation
ONEOFF_PURCHASES is highly correlated with PURCHASES and 3 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly correlated with PURCHASES and 2 other fieldsHigh correlation
CASH_ADVANCE is highly correlated with CASH_ADVANCE_FREQUENCY and 1 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly correlated with ONEOFF_PURCHASES_FREQUENCY and 2 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly correlated with ONEOFF_PURCHASES and 2 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly correlated with INSTALLMENTS_PURCHASES and 2 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly correlated with CASH_ADVANCE and 1 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly correlated with CASH_ADVANCE and 1 other fieldsHigh correlation
PURCHASES_TRX is highly correlated with PURCHASES and 5 other fieldsHigh correlation
CREDIT_LIMIT is highly correlated with BALANCEHigh correlation
PAYMENTS is highly correlated with PURCHASES and 1 other fieldsHigh correlation
BALANCE is highly correlated with MINIMUM_PAYMENTSHigh correlation
PURCHASES is highly correlated with ONEOFF_PURCHASES and 4 other fieldsHigh correlation
ONEOFF_PURCHASES is highly correlated with PURCHASES and 1 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly correlated with PURCHASES and 3 other fieldsHigh correlation
CASH_ADVANCE is highly correlated with CASH_ADVANCE_FREQUENCY and 1 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly correlated with PURCHASES and 3 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly correlated with PURCHASES and 1 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly correlated with INSTALLMENTS_PURCHASES and 2 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly correlated with CASH_ADVANCE and 1 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly correlated with CASH_ADVANCE and 1 other fieldsHigh correlation
PURCHASES_TRX is highly correlated with PURCHASES and 3 other fieldsHigh correlation
MINIMUM_PAYMENTS is highly correlated with BALANCEHigh correlation
BALANCE is highly correlated with CREDIT_LIMITHigh correlation
PURCHASES is highly correlated with ONEOFF_PURCHASES and 4 other fieldsHigh correlation
ONEOFF_PURCHASES is highly correlated with PURCHASES and 2 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly correlated with PURCHASES and 1 other fieldsHigh correlation
CASH_ADVANCE is highly correlated with CASH_ADVANCE_TRX and 1 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly correlated with ONEOFF_PURCHASES_FREQUENCY and 1 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly correlated with PURCHASES_FREQUENCYHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly correlated with PURCHASES_FREQUENCYHigh correlation
CASH_ADVANCE_FREQUENCY is highly correlated with CASH_ADVANCE_TRXHigh correlation
CASH_ADVANCE_TRX is highly correlated with CASH_ADVANCE and 1 other fieldsHigh correlation
PURCHASES_TRX is highly correlated with PURCHASES and 3 other fieldsHigh correlation
CREDIT_LIMIT is highly correlated with BALANCE and 2 other fieldsHigh correlation
PAYMENTS is highly correlated with PURCHASES and 4 other fieldsHigh correlation
MINIMUM_PAYMENTS has 313 (3.5%) missing values Missing
CUST_ID is uniformly distributed Uniform
CUST_ID has unique values Unique
PURCHASES has 2044 (22.8%) zeros Zeros
ONEOFF_PURCHASES has 4302 (48.1%) zeros Zeros
INSTALLMENTS_PURCHASES has 3916 (43.8%) zeros Zeros
CASH_ADVANCE has 4628 (51.7%) zeros Zeros
PURCHASES_FREQUENCY has 2043 (22.8%) zeros Zeros
ONEOFF_PURCHASES_FREQUENCY has 4302 (48.1%) zeros Zeros
PURCHASES_INSTALLMENTS_FREQUENCY has 3915 (43.7%) zeros Zeros
CASH_ADVANCE_FREQUENCY has 4628 (51.7%) zeros Zeros
CASH_ADVANCE_TRX has 4628 (51.7%) zeros Zeros
PURCHASES_TRX has 2044 (22.8%) zeros Zeros
PAYMENTS has 240 (2.7%) zeros Zeros
PRC_FULL_PAYMENT has 5903 (66.0%) zeros Zeros

Reproduction

Analysis started2022-04-13 05:17:00.909975
Analysis finished2022-04-13 05:17:28.506653
Duration27.6 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

CUST_ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
C10001
 
1
C16135
 
1
C16129
 
1
C16130
 
1
C16131
 
1
Other values (8945)
8945 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005

Common Values

ValueCountFrequency (%)
C100011
 
< 0.1%
C161351
 
< 0.1%
C161291
 
< 0.1%
C161301
 
< 0.1%
C161311
 
< 0.1%
C161321
 
< 0.1%
C161331
 
< 0.1%
C161341
 
< 0.1%
C161361
 
< 0.1%
C161441
 
< 0.1%
Other values (8940)8940
99.9%

Length

2022-04-13T00:17:28.547663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c100011
 
< 0.1%
c100111
 
< 0.1%
c100171
 
< 0.1%
c100161
 
< 0.1%
c100151
 
< 0.1%
c100141
 
< 0.1%
c100131
 
< 0.1%
c100121
 
< 0.1%
c100101
 
< 0.1%
c100341
 
< 0.1%
Other values (8940)8940
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BALANCE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.474828
Minimum0
Maximum19043.13856
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:28.614678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.81451835
Q1128.2819155
median873.385231
Q32054.140036
95-th percentile5909.111808
Maximum19043.13856
Range19043.13856
Interquartile range (IQR)1925.85812

Descriptive statistics

Standard deviation2081.531879
Coefficient of variation (CV)1.330498799
Kurtosis7.6747513
Mean1564.474828
Median Absolute Deviation (MAD)799.865197
Skewness2.393386043
Sum14002049.71
Variance4332774.965
MonotonicityNot monotonic
2022-04-13T00:17:28.694696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.9%
40.9007491
 
< 0.1%
1213.5513381
 
< 0.1%
1253.1883171
 
< 0.1%
5058.2996351
 
< 0.1%
296.9059441
 
< 0.1%
1084.6526471
 
< 0.1%
237.1984421
 
< 0.1%
1636.5183151
 
< 0.1%
468.8514151
 
< 0.1%
Other values (8861)8861
99.0%
ValueCountFrequency (%)
080
0.9%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0066511
 
< 0.1%
0.0096841
 
< 0.1%
0.019681
 
< 0.1%
0.0211021
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

BALANCE_FREQUENCY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8772707256
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:28.780716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.2369040027
Coefficient of variation (CV)0.2700466296
Kurtosis3.092369622
Mean0.8772707256
Median Absolute Deviation (MAD)0
Skewness-2.023265519
Sum7851.572994
Variance0.05612350649
MonotonicityNot monotonic
2022-04-13T00:17:28.874737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.818182278
 
3.1%
0.727273223
 
2.5%
0.545455219
 
2.4%
0.636364209
 
2.3%
0.454545172
 
1.9%
0.363636170
 
1.9%
0.272727151
 
1.7%
0.181818146
 
1.6%
Other values (33)761
 
8.5%
ValueCountFrequency (%)
080
0.9%
0.09090967
0.7%
0.18
 
0.1%
0.1111115
 
0.1%
0.1259
 
0.1%
0.1428577
 
0.1%
0.1666677
 
0.1%
0.181818146
1.6%
0.29
 
0.1%
0.2222225
 
0.1%
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.6%
0.85714351
 
0.6%
0.83333360
 
0.7%
0.818182278
 
3.1%
0.820
 
0.2%
0.77777822
 
0.2%

PURCHASES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.204834
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:28.956755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.634782
Coefficient of variation (CV)2.129809098
Kurtosis111.3887709
Mean1003.204834
Median Absolute Deviation (MAD)361.28
Skewness8.144269065
Sum8978683.26
Variance4565208.191
MonotonicityNot monotonic
2022-04-13T00:17:29.042774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
 
22.8%
45.6527
 
0.3%
6016
 
0.2%
15016
 
0.2%
30013
 
0.1%
20013
 
0.1%
10013
 
0.1%
45012
 
0.1%
5010
 
0.1%
60010
 
0.1%
Other values (6193)6776
75.7%
ValueCountFrequency (%)
02044
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
12
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

ONEOFF_PURCHASES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.4373709
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:29.125793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.887917
Coefficient of variation (CV)2.801794753
Kurtosis164.187572
Mean592.4373709
Median Absolute Deviation (MAD)38
Skewness10.04508288
Sum5302314.47
Variance2755227.898
MonotonicityNot monotonic
2022-04-13T00:17:29.456867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04302
48.1%
45.6546
 
0.5%
5017
 
0.2%
20015
 
0.2%
6013
 
0.1%
10013
 
0.1%
15012
 
0.1%
7012
 
0.1%
100012
 
0.1%
25011
 
0.1%
Other values (4004)4497
50.2%
ValueCountFrequency (%)
04302
48.1%
0.017
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
14
 
< 0.1%
1.42
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

INSTALLMENTS_PURCHASES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.0676447
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:29.546888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.3381152
Coefficient of variation (CV)2.199973963
Kurtosis96.57517753
Mean411.0676447
Median Absolute Deviation (MAD)89
Skewness7.299119909
Sum3679055.42
Variance817827.4266
MonotonicityNot monotonic
2022-04-13T00:17:29.626906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03916
43.8%
30014
 
0.2%
20014
 
0.2%
10014
 
0.2%
15012
 
0.1%
12511
 
0.1%
759
 
0.1%
3508
 
0.1%
4508
 
0.1%
5008
 
0.1%
Other values (4442)4936
55.2%
ValueCountFrequency (%)
03916
43.8%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.281
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

CASH_ADVANCE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.8711125
Minimum0
Maximum47137.21176
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:29.708924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.821139
95-th percentile4647.169122
Maximum47137.21176
Range47137.21176
Interquartile range (IQR)1113.821139

Descriptive statistics

Standard deviation2097.163877
Coefficient of variation (CV)2.142431062
Kurtosis52.89943411
Mean978.8711125
Median Absolute Deviation (MAD)0
Skewness5.166609074
Sum8760896.457
Variance4398096.325
MonotonicityNot monotonic
2022-04-13T00:17:29.788943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
495.4258321
 
< 0.1%
1486.2432931
 
< 0.1%
855.2327791
 
< 0.1%
3767.1047071
 
< 0.1%
291.6085121
 
< 0.1%
38.6905521
 
< 0.1%
521.6643691
 
< 0.1%
1974.2029631
 
< 0.1%
2462.1007891
 
< 0.1%
Other values (4313)4313
48.2%
ValueCountFrequency (%)
04628
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

PURCHASES_FREQUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4903505484
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:29.870960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4013707474
Coefficient of variation (CV)0.8185383879
Kurtosis-1.638630948
Mean0.4903505484
Median Absolute Deviation (MAD)0.416667
Skewness0.06016423586
Sum4388.637408
Variance0.1610984768
MonotonicityNot monotonic
2022-04-13T00:17:29.952979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12178
24.3%
02043
22.8%
0.083333677
 
7.6%
0.916667396
 
4.4%
0.5395
 
4.4%
0.166667392
 
4.4%
0.833333373
 
4.2%
0.333333367
 
4.1%
0.25345
 
3.9%
0.583333316
 
3.5%
Other values (37)1468
16.4%
ValueCountFrequency (%)
02043
22.8%
0.083333677
 
7.6%
0.09090943
 
0.5%
0.127
 
0.3%
0.11111118
 
0.2%
0.12532
 
0.4%
0.14285726
 
0.3%
0.166667392
 
4.4%
0.18181816
 
0.2%
0.219
 
0.2%
ValueCountFrequency (%)
12178
24.3%
0.916667396
 
4.4%
0.90909128
 
0.3%
0.924
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714325
 
0.3%
0.833333373
 
4.2%
0.81818221
 
0.2%
0.89
 
0.1%

ONEOFF_PURCHASES_FREQUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2024576836
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.035998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2983360652
Coefficient of variation (CV)1.473572452
Kurtosis1.161845601
Mean0.2024576836
Median Absolute Deviation (MAD)0.083333
Skewness1.535612784
Sum1811.996268
Variance0.08900440779
MonotonicityNot monotonic
2022-04-13T00:17:30.117016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.166667592
 
6.6%
1481
 
5.4%
0.25418
 
4.7%
0.333333355
 
4.0%
0.416667244
 
2.7%
0.5235
 
2.6%
0.583333197
 
2.2%
0.666667167
 
1.9%
Other values (37)855
 
9.6%
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.09090956
 
0.6%
0.139
 
0.4%
0.11111126
 
0.3%
0.12541
 
0.5%
0.14285737
 
0.4%
0.166667592
 
6.6%
0.18181834
 
0.4%
0.227
 
0.3%
ValueCountFrequency (%)
1481
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333120
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

PURCHASES_INSTALLMENTS_FREQUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3644373416
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.197034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.3974477797
Coefficient of variation (CV)1.090579187
Kurtosis-1.398632185
Mean0.3644373416
Median Absolute Deviation (MAD)0.166667
Skewness0.509201165
Sum3261.714207
Variance0.1579647376
MonotonicityNot monotonic
2022-04-13T00:17:30.280052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03915
43.7%
11331
 
14.9%
0.416667388
 
4.3%
0.916667345
 
3.9%
0.833333311
 
3.5%
0.5310
 
3.5%
0.166667305
 
3.4%
0.666667292
 
3.3%
0.75291
 
3.3%
0.083333275
 
3.1%
Other values (37)1187
 
13.3%
ValueCountFrequency (%)
03915
43.7%
0.083333275
 
3.1%
0.09090912
 
0.1%
0.16
 
0.1%
0.1111119
 
0.1%
0.1255
 
0.1%
0.1428576
 
0.1%
0.166667305
 
3.4%
0.18181814
 
0.2%
0.29
 
0.1%
ValueCountFrequency (%)
11331
14.9%
0.916667345
 
3.9%
0.90909125
 
0.3%
0.919
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714330
 
0.3%
0.833333311
 
3.5%
0.81818221
 
0.2%
0.818
 
0.2%

CASH_ADVANCE_FREQUENCY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442003
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.365072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.2001213881
Coefficient of variation (CV)1.480798937
Kurtosis3.334734328
Mean0.1351442003
Median Absolute Deviation (MAD)0
Skewness1.828686266
Sum1209.540593
Variance0.04004856999
MonotonicityNot monotonic
2022-04-13T00:17:30.444090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.166667759
 
8.5%
0.25578
 
6.5%
0.333333439
 
4.9%
0.416667273
 
3.1%
0.5215
 
2.4%
0.583333142
 
1.6%
0.666667125
 
1.4%
0.09090970
 
0.8%
Other values (44)700
 
7.8%
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.09090970
 
0.8%
0.139
 
0.4%
0.11111129
 
0.3%
0.12547
 
0.5%
0.14285749
 
0.5%
0.166667759
 
8.5%
0.18181842
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
1.51
 
< 0.1%
1.251
 
< 0.1%
1.1666672
 
< 0.1%
1.1428571
 
< 0.1%
1.1251
 
< 0.1%
1.11
 
< 0.1%
1.0909091
 
< 0.1%
125
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%

CASH_ADVANCE_TRX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.248826816
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.526108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.824646744
Coefficient of variation (CV)2.100649598
Kurtosis61.64686248
Mean3.248826816
Median Absolute Deviation (MAD)0
Skewness5.721298203
Sum29077
Variance46.57580318
MonotonicityNot monotonic
2022-04-13T00:17:30.609126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
10150
 
1.7%
Other values (55)915
 
10.2%
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
9111
 
1.2%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

PURCHASES_TRX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.7098324
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.710149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.85764911
Coefficient of variation (CV)1.689866236
Kurtosis34.79310026
Mean14.7098324
Median Absolute Deviation (MAD)7
Skewness4.630655266
Sum131653
Variance617.9027193
MonotonicityNot monotonic
2022-04-13T00:17:30.795168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
12570
 
6.4%
2379
 
4.2%
6352
 
3.9%
3314
 
3.5%
4285
 
3.2%
7275
 
3.1%
5267
 
3.0%
8267
 
3.0%
Other values (163)3530
39.4%
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
2379
 
4.2%
3314
 
3.5%
4285
 
3.2%
5267
 
3.0%
6352
 
3.9%
7275
 
3.1%
8267
 
3.0%
9248
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

CREDIT_LIMIT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct205
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4494.44945
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:30.894191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.815725
Coefficient of variation (CV)0.8096243524
Kurtosis2.836655932
Mean4494.44945
Median Absolute Deviation (MAD)1800
Skewness1.522464005
Sum40220828.13
Variance13240979.88
MonotonicityNot monotonic
2022-04-13T00:17:30.990212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000784
 
8.8%
1500722
 
8.1%
1200621
 
6.9%
1000614
 
6.9%
2500612
 
6.8%
4000506
 
5.7%
6000463
 
5.2%
5000389
 
4.3%
2000371
 
4.1%
7500277
 
3.1%
Other values (195)3590
40.1%
ValueCountFrequency (%)
501
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500121
1.4%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

PAYMENTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.143852
Minimum0
Maximum50721.48336
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:31.079232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.98892395
Q1383.276166
median856.901546
Q31901.134317
95-th percentile6082.090595
Maximum50721.48336
Range50721.48336
Interquartile range (IQR)1517.858151

Descriptive statistics

Standard deviation2895.063757
Coefficient of variation (CV)1.670411693
Kurtosis54.77073581
Mean1733.143852
Median Absolute Deviation (MAD)581.3514605
Skewness5.907619794
Sum15511637.48
Variance8381394.157
MonotonicityNot monotonic
2022-04-13T00:17:31.162251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
2.7%
201.8020841
 
< 0.1%
398.3164411
 
< 0.1%
826.0367481
 
< 0.1%
2571.5732141
 
< 0.1%
1903.2796431
 
< 0.1%
454.8885061
 
< 0.1%
956.0287471
 
< 0.1%
4560.775721
 
< 0.1%
1825.3499551
 
< 0.1%
Other values (8701)8701
97.2%
ValueCountFrequency (%)
0240
2.7%
0.0495131
 
< 0.1%
0.0564661
 
< 0.1%
2.3895831
 
< 0.1%
3.5005051
 
< 0.1%
4.5235551
 
< 0.1%
4.8415431
 
< 0.1%
5.0707261
 
< 0.1%
9.0400171
 
< 0.1%
9.5333131
 
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

MINIMUM_PAYMENTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8636
Distinct (%)> 99.9%
Missing313
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean864.2065423
Minimum0.019163
Maximum76406.20752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:31.249270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.2820058
Q1169.123707
median312.343947
Q3825.485459
95-th percentile2766.56331
Maximum76406.20752
Range76406.18836
Interquartile range (IQR)656.361752

Descriptive statistics

Standard deviation2372.446607
Coefficient of variation (CV)2.745231019
Kurtosis283.9899859
Mean864.2065423
Median Absolute Deviation (MAD)190.374096
Skewness13.62279699
Sum7464151.906
Variance5628502.901
MonotonicityNot monotonic
2022-04-13T00:17:31.601350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.3518812
 
< 0.1%
150.3171431
 
< 0.1%
271.5281691
 
< 0.1%
6404.8554841
 
< 0.1%
616.8625441
 
< 0.1%
211.9841931
 
< 0.1%
324.9547471
 
< 0.1%
1600.269171
 
< 0.1%
277.5467131
 
< 0.1%
342.286491
 
< 0.1%
Other values (8626)8626
96.4%
(Missing)313
 
3.5%
ValueCountFrequency (%)
0.0191631
< 0.1%
0.0377441
< 0.1%
0.055881
< 0.1%
0.0594811
< 0.1%
0.1170361
< 0.1%
0.2619841
< 0.1%
0.3119531
< 0.1%
0.3194751
< 0.1%
1.1130271
< 0.1%
1.3340751
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

PRC_FULL_PAYMENT
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1537146485
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:31.689370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924991962
Coefficient of variation (CV)1.902871321
Kurtosis2.432395301
Mean0.1537146485
Median Absolute Deviation (MAD)0
Skewness1.942819941
Sum1375.746104
Variance0.0855557798
MonotonicityNot monotonic
2022-04-13T00:17:31.772414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05903
66.0%
1488
 
5.5%
0.083333426
 
4.8%
0.166667166
 
1.9%
0.5156
 
1.7%
0.25156
 
1.7%
0.090909153
 
1.7%
0.333333134
 
1.5%
0.194
 
1.1%
0.283
 
0.9%
Other values (37)1191
 
13.3%
ValueCountFrequency (%)
05903
66.0%
0.083333426
 
4.8%
0.090909153
 
1.7%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.8%
0.283
 
0.9%
ValueCountFrequency (%)
1488
5.5%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333363
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

TENURE
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.51731844
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2022-04-13T00:17:31.838421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.338330769
Coefficient of variation (CV)0.1162015947
Kurtosis7.694823186
Mean11.51731844
Median Absolute Deviation (MAD)0
Skewness-2.943017288
Sum103080
Variance1.791129248
MonotonicityNot monotonic
2022-04-13T00:17:31.890433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
6204
 
2.3%
8196
 
2.2%
7190
 
2.1%
9175
 
2.0%
ValueCountFrequency (%)
6204
 
2.3%
7190
 
2.1%
8196
 
2.2%
9175
 
2.0%
10236
 
2.6%
11365
 
4.1%
127584
84.7%
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
9175
 
2.0%
8196
 
2.2%
7190
 
2.1%
6204
 
2.3%

Interactions

2022-04-13T00:17:26.746258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:04.390759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:12.739639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:21.289561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:25.384952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:26.963306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:23.217995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:24.763343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:26.044100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:27.627456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:05.580026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:06.864316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:08.115597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:09.678949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:10.923229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:12.385558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:13.618838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:15.133176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:16.394460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:17.939808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:19.213094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:20.756441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:22.042730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:23.294012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:24.842360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:26.119117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:27.702473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:05.662045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:06.939334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:08.438671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:09.754968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:10.994245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:12.455573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:13.691853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:15.206193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:18.013824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:19.286110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:20.829457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:22.116747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:23.366028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:24.920378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:26.193134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:27.772490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:05.742063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:07.010349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:08.508687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:09.828984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:11.063260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:12.522589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:13.761868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:15.277209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:16.540493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:18.087841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:23.687100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:18.167859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:19.435144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:20.992494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:22.266780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:25.085414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T00:17:27.919522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:05.910101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:07.161384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:08.660721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:09.987019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:11.467351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:12.668622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:13.913903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:15.434244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:16.694527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:18.241875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:19.507160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:21.066511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:22.341798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:23.837134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:25.161432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T00:17:26.411182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-13T00:17:31.960449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T00:17:32.094478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T00:17:32.229509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T00:17:32.364539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T00:17:28.042549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T00:17:28.233592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-13T00:17:28.360621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-13T00:17:28.418634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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